plot.MatrixEQTL: Plot histogram or QQ-plot of all p-values

In MatrixEQTL: Matrix eQTL: Ultra Fast eQTL Analysis via Large Matrix Operations

Description

This method plots a histogram or QQ-plot of p-values for all tests performed by Matrix_eQTL_engine.

Usage

## S3 method for class 'MatrixEQTL'
plot(
    x,
    cex = 0.5,
    pch = 19,
    xlim = NULL,
    ylim = NULL,
    main = NULL,
    ...)

Arguments

x	An object returned by Matrix_eQTL_engine.
cex	A numerical value giving the amount by which plotting text and symbols should be magnified relative to the default.
pch	Plotting "character", i.e., symbol to use. See points.
xlim	Set the range of the horisontal axis.
ylim	Set the range of the vertical axis.
main	Plot title.
...	Further graphical parameters passed to lines and points.

Details

The plot type (histogram vs. QQ-plot) is determined by the pvalue.hist parameter in the call of Matrix_eQTL_engine function.

Value

The method does not return any value.

Note

The sample code below produces figures like these:

Histogram:

QQ-plot:

Author(s)

Andrey A Shabalin [e-mail]

References

The package website: http://www.bios.unc.edu/research/genomic_software/Matrix_eQTL/

See Also

See Matrix_eQTL_engine for reference and sample code.

Examples

library(MatrixEQTL)

# Number of samples
n = 100 

# Number of variables
ngs = 2000

# Common signal in all variables
pop = 0.2*rnorm(n)

# data matrices
snps.mat = matrix(rnorm(n*ngs), ncol = ngs) + pop
gene.mat = matrix(rnorm(n*ngs), ncol = ngs) + pop + snps.mat*((1:ngs)/ngs)^9/2

# data objects for Matrix eQTL engine
snps1 = SlicedData$new( t( snps.mat ) )
gene1 = SlicedData$new( t( gene.mat ) )
cvrt1 = SlicedData$new( )
rm(snps.mat, gene.mat)

# Slice data in blocks of 500 variables
snps1$ResliceCombined(500)
gene1$ResliceCombined(500)

# Produce no output files
filename = NULL # tempfile()

# Perform analysis recording information for a histogram
meh = Matrix_eQTL_engine(
    snps = snps1, 
    gene = gene1, 
    cvrt = cvrt1, 
    output_file_name = filename, 
    pvOutputThreshold = 1e-100, 
    useModel = modelLINEAR, 
    errorCovariance = numeric(), 
    verbose = TRUE,
    pvalue.hist = 100)
    
plot(meh, col="grey")

# Perform analysis recording information for a QQ-plot
meq = Matrix_eQTL_engine(
    snps = snps1, 
    gene = gene1, 
    cvrt = cvrt1, 
    output_file_name = filename,
    pvOutputThreshold = 1e-6, 
    useModel = modelLINEAR, 
    errorCovariance = numeric(), 
    verbose = TRUE,
    pvalue.hist = "qqplot")
    
plot(meq)

Example output

Processing covariates 
Task finished in  0.001  seconds
Processing gene expression data (imputation, residualization, etc.) 
Task finished in  0.019  seconds
Creating output file(s) 
Task finished in  0.006  seconds
Performing eQTL analysis 
 6.25% done, 0 eQTLs
12.50% done, 0 eQTLs
18.75% done, 0 eQTLs
25.00% done, 0 eQTLs
31.25% done, 0 eQTLs
37.50% done, 0 eQTLs
43.75% done, 0 eQTLs
50.00% done, 0 eQTLs
56.25% done, 0 eQTLs
62.50% done, 0 eQTLs
68.75% done, 0 eQTLs
75.00% done, 0 eQTLs
81.25% done, 0 eQTLs
87.50% done, 0 eQTLs
93.75% done, 0 eQTLs
100.00% done, 0 eQTLs
No significant associations were found.
Task finished in  0.655  seconds
 
Processing covariates 
Task finished in  0  seconds
Processing gene expression data (imputation, residualization, etc.) 
Task finished in  0.01  seconds
Creating output file(s) 
Task finished in  0.013  seconds
Performing eQTL analysis 
 6.25% done, 8 eQTLs
12.50% done, 10 eQTLs
18.75% done, 10 eQTLs
25.00% done, 11 eQTLs
31.25% done, 14 eQTLs
37.50% done, 23 eQTLs
43.75% done, 23 eQTLs
50.00% done, 25 eQTLs
56.25% done, 25 eQTLs
62.50% done, 26 eQTLs
68.75% done, 32 eQTLs
75.00% done, 35 eQTLs
81.25% done, 35 eQTLs
87.50% done, 37 eQTLs
93.75% done, 38 eQTLs
100.00% done, 47 eQTLs
Task finished in  0.605  seconds
 

MatrixEQTL documentation built on Dec. 22, 2019, 5:06 p.m.